To date pre-clinical and clinical applications of mass spectrometry (MS)-based proteomic techniques analyzing complex biofluids have fallen short of expectations, largely due to deficiencies in both analytical sensitivity and throughput. These deficiencies result in measurements typically failing to confidently detect and quantify proteins at moderate to low concentrations, or not providing sufficient sample analysis throughput for statistical relevance. Higher sensitivity targeted MS analyses are currently utilized to address these shortcomings [1, 2]; however, these often only analyze a small list of proteins identified as biologically significant. While targeted MS measurements are increasingly common in clinical applications [3, 4], the limited number of proteins they examine does not necessarily reflect the biodiversity across a population, making broad untargeted measurements useful in developing individual disease metrics for diagnosis [5]. As the future of medicine proceeds toward a personal profiling approach [6, 7], the potential for robust high throughput clinical measurements based upon MS is highly attractive if its deficiencies can be addressed.
An initial step in attaining broad untargeted measurements that increasingly retain the benefits of targeted analyses exploits technological advances such as faster separations, more effective ion sources, detectors with greater dynamic range, and MS measurements with both higher resolution and accuracy. Advanced liquid-phase separations have already been employed to provide a significant sensitivity increase as illustrated by the higher number of proteins detected in liquid chromatography (LC)-MS-based studies [8]; however, the long LC separations most compatible with blood samples are extremely time-consuming. Fast gas-phase ion mobility spectrometry (IMS) separations that take place on the time scale of tens of milliseconds offer an additional separation stage and a way of reducing the need for extended LC separation times. In an IMS separation, ions subject to an electric field while traveling through a buffer gas separate quickly based on ion shape, e.g. compact species drift faster than those with extended structures [9, 10]. IMS can be coupled between LC and orthogonal acceleration time-of-flight (TOF) MS stages, and by combining the three orthogonal separations into a single LC-IMS-MS instrumentation platform, multidimensional high-resolution nested spectra are produced containing elution times, mass-to-charge ratios (m/z) and IMS drift times for all detectable ions in a sample [11, 12]
Liver fibrosis may result from a wide variety of conditions including chronic alcohol exposure, hepatitis B virus (HBV) infection, non-alcoholic fatty liver disease (NAFLD), hepatitis C virus (HCV) infection, Wilson's disease, alpha-1-antitrypsin deficiency, hemochromatosis, primary biliary cirrhosis, primary sclerosing cholangitis, and autoimmune hepatitis. Chronic HCV is the leading contributor to chronic liver disease and represents a worldwide public health concern affecting an estimated 130-170 million people [16]. The liver damage ensuing from HCV infection is also the leading cause of liver transplants in the United States and Europe and a major burden on healthcare services [17, 18]. In this disease, the liver elicits a persistent inflammatory and repair response known as fibrosis, which is characterized by the formation of fibrous tissue and scarring on the liver. Because the prognosis of HCV patients is related to the development of fibrosis and the risk of cirrhosis and hepatocellular carcinoma, an accurate evaluation of fibrogenic progression is important for patient care.
Currently, liver biopsies are the primary technique for generating information on the degree of fibrosis; however, they have multiple disadvantages, including risk of complications (e.g., major bleeding or inadvertent puncture of the lung, kidney, or colon), cost and occasionally inaccurate findings due to small specimen size and variability in histology evaluation. These disadvantages have spurred the development of noninvasive methods that can reliably predict, diagnose and assess the degree of fibrosis [19, 20].